1. Field
One feature relates to computer vision, and more particularly, to methods and techniques for improving performance and/or efficiency of image recognition systems.
2. Background
Various applications may benefit from having a machine or processor that is capable of identifying objects in a visual representation (e.g., an image or picture). The field of computer vision attempts to provide techniques and/or algorithms that permit identifying objects or features in an image, where an object or feature may be characterized by descriptors identifying one or more keypoints. These techniques and/or algorithms, such as SIFT (Scale Invariant Feature Transform), are often also applied to image recognition, object detection, image matching, 3-dimensional structure construction, stereo correspondence, and/or motion tracking, face recognition, among other applications.
Generally, object or feature recognition may involve identifying points of interest (also called keypoints) in an image and/or localized features around those keypoints for the purpose of feature identification, image retrieval, and/or object recognition. Having high stability and repeatability of features is of great importance in these recognition algorithms. Thus, the keypoints may be selected and/or processed such that they are invariant to image scale changes and/or rotation and provide robust matching across a substantial range of distortions, changes in point of view, and/or noise and change in illumination. Further, in order to be well suited for tasks such as image retrieval and object recognition, the feature descriptors may preferably be distinctive in the sense that a single feature can be correctly matched with high probability against a large database of features from a plurality of target images.
After the keypoints in an image are detected and located, they may be identified or described by using various descriptors. For example, descriptors may represent the visual features of the content in images, such as shape, color, texture, rotation, and/or motion, among other image characteristics. The individual features corresponding to the keypoints and represented by the descriptors are then matched to a database of features from known objects.
As part of identifying and selecting keypoints for an image, some points that have been selected may need to be discarded due to lack of precision or confidence. For instance, some initially detected keypoints may be rejected on the grounds of poor contrast and/or poor localization along edges. Such rejections are important in increasing keypoint stability with respect to illumination, noise and orientation variations. It is also important to minimize false keypoint rejections which would decrease repeatability of feature matching. However, having spatially varying illumination changes poses a significant problem for feature detection since effects such as shadowing can effectively cause interesting features to be ignored entirely, decreasing repeatability in object recognition.
Therefore, a method or approach is needed to define thresholds that are adaptive to local and global illumination changes for feature selection within object recognition algorithms.